Code Trans T5 Small Source Code Summarization Sql

🚀 CodeTrans Model for Source Code Summarization in SQL

This is a pre - trained model for the SQL programming language, leveraging the T5 small model architecture. It was initially released in this repository. This model is trained on tokenized SQL code functions, and it performs optimally with tokenized SQL functions.

✨ Features

• Based on the t5 - small model with its own SentencePiece vocabulary model.
• Trained using single - task training on a source code summarization SQL dataset.
• Can generate descriptions for SQL functions or be fine - tuned for other SQL code tasks.
• Works on unparsed and untokenized SQL code, but performs better with tokenized code.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

Here is how to use this model to generate SQL function documentation using Transformers SummarizationPipeline:

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql", skip_special_tokens=True),
    device=0
)

tokenized_code = "select time ( col0 ) from tab0"
pipeline([tokenized_code])

Run this example in colab notebook.

📚 Documentation

Model description

This CodeTrans model is based on the t5 - small model. It has its own SentencePiece vocabulary model. It used single - task training on source code summarization sql dataset.

Intended uses & limitations

The model could be used to generate the description for the sql function or be fine - tuned on other sql code tasks. It can be used on unparsed and untokenized sql code. However, if the sql code is tokenized, the performance should be better.

🔧 Technical Details

Training data

The supervised training tasks datasets can be downloaded on Link

Evaluation results

For the source code summarization tasks, different models achieve the following results on different programming languages (in BLEU score):

Test results :

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn